DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Yeji | ko |
dc.contributor.author | Jeong, Yoonho | ko |
dc.contributor.author | Kim, Jihoo | ko |
dc.contributor.author | Lee, Eok Kyun | ko |
dc.contributor.author | Kim, Won June | ko |
dc.contributor.author | Choi, Insung S. | ko |
dc.date.accessioned | 2022-08-26T01:01:00Z | - |
dc.date.available | 2022-08-26T01:01:00Z | - |
dc.date.created | 2022-08-01 | - |
dc.date.created | 2022-08-01 | - |
dc.date.created | 2022-08-01 | - |
dc.date.issued | 2022-08 | - |
dc.identifier.citation | CHEMISTRY-AN ASIAN JOURNAL, v.17, no.16 | - |
dc.identifier.issn | 1861-4728 | - |
dc.identifier.uri | http://hdl.handle.net/10203/298128 | - |
dc.description.abstract | Most graph neural networks (GNNs) in deep-learning chemistry collect and update atom and molecule features from the fed atom (and, in some cases, bond) features, basically based on the two-dimensional (2D) graph representation of 3D molecules. However, the 2D-based models do not faithfully represent 3D molecules and their physicochemical properties, exemplified by the overlooked field effect that is a “through-space” effect, not a “through-bond” effect. We propose a GNN model, denoted as MolNet, which accommodates the 3D non-bond information in a molecule, via a noncovalent adjacency matrix (Formula presented.), and also bond-strength information from a weighted bond matrix (Formula presented.). Comparative studies show that MolNet outperforms various baseline GNN models and gives a state-of-the-art performance in the classification task of BACE dataset and regression task of ESOL dataset. This work suggests a future direction for the construction of deep-learning models that are chemically intuitive and compatible with the existing chemistry concepts and tools. | - |
dc.language | English | - |
dc.publisher | WILEY-V C H VERLAG GMBH | - |
dc.title | MolNet: A Chemically Intuitive Graph Neural Network for Prediction of Molecular Properties | - |
dc.type | Article | - |
dc.identifier.wosid | 000827678200001 | - |
dc.identifier.scopusid | 2-s2.0-85135095705 | - |
dc.type.rims | ART | - |
dc.citation.volume | 17 | - |
dc.citation.issue | 16 | - |
dc.citation.publicationname | CHEMISTRY-AN ASIAN JOURNAL | - |
dc.identifier.doi | 10.1002/asia.202200269 | - |
dc.contributor.localauthor | Lee, Eok Kyun | - |
dc.contributor.localauthor | Choi, Insung S. | - |
dc.contributor.nonIdAuthor | Kim, Yeji | - |
dc.contributor.nonIdAuthor | Jeong, Yoonho | - |
dc.contributor.nonIdAuthor | Kim, Won June | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | adjacency matrix | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | graph neural networks | - |
dc.subject.keywordAuthor | molecular representation | - |
dc.subject.keywordAuthor | protein-ligand binding | - |
dc.subject.keywordPlus | FIELD | - |
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